


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
			from PIL import Image
else:

			class 							a						:




										"""simple docstring"""


										@staticmethod
										def        __snake_case   (				*lowerCamelCase			:    Any       ,      **lowerCamelCase			:    Union[str, Any]							)      ->       Tuple:
																	pass







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     Union[str, Any]									=  hashlib.mda(image.tobytes()      )
							return m.hexdigest()[:1_0]







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     int									=  np.array(__lowerCamelCase      )
							__snake_case						:     str									=  npimg.shape
							return {"hash": hashimage(__lowerCamelCase      ), "shape": shape}

@is_pipeline_test
@require_vision
@require_torch
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Union[str, Any]	    =    dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
							__UpperCAmelCase       :				Dict	    =    dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    List[Any]							)      ->       Dict:
														__snake_case						:     str									=  MaskGenerationPipeline(model=lowerCamelCase       ,      image_processor=lowerCamelCase							)
														return image_segmenter, [
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														    "./tests/fixtures/tests_samples/COCO/000000039769.png",
														]

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[int]							)      ->       str:
														pass

							@require_tf
							@unittest.skip("Image segmentation not implemented in TF"							)
							def        __snake_case   (				self			:    str							)      ->       Tuple:
														pass

							@slow
							@require_torch
							def        __snake_case   (				self			:    Optional[Any]							)      ->       int:
														__snake_case						:     List[Any]									=  pipeline("mask-generation"       ,      model="facebook/sam-vit-huge"							)

														__snake_case						:     Optional[int]									=  image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,      points_per_batch=256							)

														# Shortening by hashing
														__snake_case						:     List[str]									=  []
														for i, o in enumerate(outputs["masks"]							):
																					new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase							), "scores": outputs["scores"][i]}]

														# fmt: off
														self.assertEqual(
														    nested_simplify(lowerCamelCase       ,      decimals=4							)       ,      [
														        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
														        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
														        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
														        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
														        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
														        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
														        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
														        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
														        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
														        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
														        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
														        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
														        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
														        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
														        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
														        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
														        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
														        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
														        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
														        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
														        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
														        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
														        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
														        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
														        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
														        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
														        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
														        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
														        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
														        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
														    ]       ,      )
														# fmt: on






							@require_torch
							@slow
							def        __snake_case   (				self			:    List[Any]							)      ->       List[str]:
														__snake_case						:     List[Any]									=  "facebook/sam-vit-huge"
														__snake_case						:     Union[str, Any]									=  pipeline("mask-generation"       ,      model=lowerCamelCase							)

														__snake_case						:     str									=  image_segmenter(
														    "http://images.cocodataset.org/val2017/000000039769.jpg"       ,      pred_iou_thresh=1       ,      points_per_batch=256							)

														# Shortening by hashing
														__snake_case						:     Tuple									=  []
														for i, o in enumerate(outputs["masks"]							):
																					new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase							), "scores": outputs["scores"][i]}]

														self.assertEqual(
														    nested_simplify(lowerCamelCase       ,      decimals=4							)       ,      [
														        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
														        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
														        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
														        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
														        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
														    ]       ,      )






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






